comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@gmux_CPD/convert_to_pot.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function pot = convert_to_pot(CPD, pot_type, domain, evidence)
+% CONVERT_TO_POT Convert a gmux CPD to a Gaussian potential
+% pot = convert_to_pot(CPD, pot_type, domain, evidence)
+  
+switch pot_type
+ case {'d', 'u', 'cg', 'scg'},
+  error(['can''t convert gmux to potential of type ' pot_type])
+
+ case {'c','g'},
+  % We create a large weight matrix with zeros in all blocks corresponding
+  % to the non-chosen parents, since they are effectively disconnected.
+  % The chosen parent is determined by the value, m,  of the discrete parent.
+  % Thus the potential is as large as the whole family.
+  ps = domain(1:end-1);
+  dps = ps(CPD.dps); % CPD.dps is an index, not a node number (because of param tying)
+  cps = ps(CPD.cps);
+  m = evidence{dps};
+  if isempty(m)
+    error('gmux node must have observed discrete parent')
+  end
+  bs = CPD.sizes(CPD.cps);
+  b = block(m, bs);
+  sum_cpsz = sum(CPD.sizes(CPD.cps));
+  selfsz = CPD.sizes(end);
+  W = zeros(selfsz, sum_cpsz);
+  W(:,b) = CPD.weights(:,:,m);
+
+  ns = zeros(1, max(domain));
+  ns(domain) = CPD.sizes;
+  self = domain(end);
+  cdom = [cps(:)' self];
+  pot = linear_gaussian_to_cpot(CPD.mean(:,m), CPD.cov(:,:,m), W, domain, ns, cdom, evidence);
+  
+ otherwise,
+  error(['unrecognized pot_type' pot_type])
+end
+